In general, the problem of learning a dynamical model is formulated as learning a predictive mapping from the present state and action to the future state [1]}. Usually, the outputs and the inputs in learning of this mapping are treated as general Euclidean vectors. However, such a view overlooks the intrinsic properties within the state space of a dynamical system. In fact, the states form a manifold structure [2]}, [3]}. In this work we propose a novel approach to exploit this underlying structure and learn reliable dynamical models.
